---
title: "Analyzing Clean Eating Month Results"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(tidyr)
library(dplyr)
library(ggplot2)
library(tidyverse)
library(ggcorrplot)
library(heatmaply)
library(ggthemes)
library(glue)
library(shiny)
# global constants
RAW_DATA_DIR <- "./raw_data/"
RAW_DATA_FILE_NAME <- "data.csv"

# read the data
df <- read_csv(file.path(RAW_DATA_DIR, RAW_DATA_FILE_NAME))

df <- df %>%
  select(-`muscle-percentage`, -`lean-mass`) %>%
  drop_na() %>%
  arrange(date)
# what does it look like?
summary(df)
```

Column {data-width=600}
-----------------------------------------------------------------------

### General Trend
```{r}
renderPlot({
  df_tidy <- df %>%
  gather(metric, value, -date, -name)
p <- df_tidy %>%
  ggplot(aes(x=date, y=value, col=name)) +
  geom_point() +
  geom_smooth(method = "lm") +
  facet_wrap(~metric, scales="free_y") + 
  #facet_grid(metric~name, scales="free_y") +
  theme_bw() +
  theme(legend.position = "bottom")
p
  
})

```


Column {data-width=400}
-----------------------------------------------------------------------
### Description
The sunburst diagram has the high schools in the innermost donut chart, followed the community college followed by the course name. Hover over the outermost layer to see the complete sequence from High School -> Community College -> Course. 
```{r}
renderPlot({
  df_change_amit <- df %>%
  gather(key, value, -name, -date) %>%
  filter(name=="Amit") %>%
  filter(date==min(date) | date==max(date)) %>%
  spread(date, value)
colnames(df_change_amit) <- c("name", "metric", "value_at_start", "value_at_end")
df_change_amit <- df_change_amit %>%
  mutate(abs_change = abs(value_at_end-value_at_start)) %>%
  select(-value_at_end, -value_at_start)
  

df_change_nidhi <- df %>%
  gather(key, value, -name, -date) %>%
  filter(name=="Nidhi") %>%
  filter(date==min(date) | date==max(date)) %>%
  spread(date, value)
colnames(df_change_nidhi) <- c("name", "metric", "value_at_start", "value_at_end")
df_change_nidhi
df_change_nidhi <- df_change_nidhi %>%
  mutate(abs_change = -abs(value_at_end-value_at_start)) %>%
  select(-value_at_end, -value_at_start)

df_change <- bind_rows(df_change_amit, df_change_nidhi) %>%
  arrange((abs_change))

df_change$metric <- factor(df_change$metric, levels = c("weight", "bmi", "total-lean-mass-percentage", "body-fat-percentage", "hydration-percentage"))
df_change <- df_change %>%
  arrange(desc(metric))
# X Axis Breaks and Labels 
brks <- seq(-15, 15, 1)
lbls = paste0(as.character(c(seq(15, 0, -1), seq(1, 15, 1))), "")

# Plot
ggplot(df_change , aes(x = metric, y = abs_change, fill = name)) +   # Fill column
  geom_bar(stat = "identity", width = .6) +   # draw the bars
  scale_y_continuous(breaks = brks,   # Breaks
                     labels = lbls) + # Labels
  coord_flip() +  # Flip axes
  labs(title="Email Campaign Funnel") +
  theme_tufte() +  # Tufte theme from ggfortify
  theme(plot.title = element_text(hjust = .5), 
        axis.ticks = element_blank()) +   # Centre plot title
  scale_fill_brewer(palette = "Dark2")  # Color palette
  
})

```

### Sankey diagram

Provides an alternate visualization for the same dataset. Click on the image to open the Sankey diagram. 
```{r}
renderPlot({
  df_wt_loss <- df %>%
  select(name, date, weight) %>%
  group_by(name) %>%
  arrange(date, .by_group=TRUE) %>%
  mutate(loss_per_day = weight-lag(weight, 1)) 

df_wt_loss %>%
  drop_na() %>%
  group_by(name) %>%
  summarize(median_wt_loss_per_day=median(loss_per_day))
median_wt_loss_per_day_amit <- round(median(df_wt_loss[df_wt_loss$name == "Amit", ]$loss_per_day, na.rm=TRUE), 2)
median_wt_loss_per_day_nidhi <- round(median(df_wt_loss[df_wt_loss$name == "Nidhi", ]$loss_per_day, na.rm=TRUE), 2)


p <- df_wt_loss %>%
  drop_na() %>%
  ggplot(aes(x=name, y=loss_per_day)) + 
  geom_boxplot() + 
  geom_dotplot(binaxis='y', 
               stackdir='center', 
               dotsize = .5, 
               aes(fill=name)) +
  #theme(axis.text.x = element_text(angle=0, vjust=0.6)) + 
  labs(title="Distribution of per day weight loss (in pounds)", 
       subtitle=glue("Median weight loss per day, Amit={median_wt_loss_per_day_amit} pounds, Nidhi={median_wt_loss_per_day_nidhi} pounds"),
       caption="Source: Data collected during clean eating 30day challenge",
       x="",
       y="Weight loss per day in pounds (-ve represents loss)") + 
  theme(legend.position = "none")
p
  
})
 
```